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Semi-supervised classification based on subspace sparse representation

Yu, Guoxian, Zhang, Guoji, Zhang, Zili, Yu, Zhiwen and Deng, Lin 2015, Semi-supervised classification based on subspace sparse representation, Knowledge and information systems, vol. 43, no. 1, pp. 81-101, doi: 10.1007/s10115-013-0702-2.

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Title Semi-supervised classification based on subspace sparse representation
Author(s) Yu, Guoxian
Zhang, Guoji
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Yu, Zhiwen
Deng, Lin
Journal name Knowledge and information systems
Volume number 43
Issue number 1
Start page 81
End page 101
Total pages 21
Publisher Springer
Place of publication Berlin, Germany
Publication date 2015-04
ISSN 0219-1377
0219-3116
Keyword(s) Graph construction
High-dimensional data
Semi-supervised classification
Subspaces sparse representation
Summary Graph plays an important role in graph-based semi-supervised classification. However, due to noisy and redundant features in high-dimensional data, it is not a trivial job to construct a well-structured graph on high-dimensional samples. In this paper, we take advantage of sparse representation in random subspaces for graph construction and propose a method called Semi-Supervised Classification based on Subspace Sparse Representation, SSC-SSR in short. SSC-SSR first generates several random subspaces from the original space and then seeks sparse representation coefficients in these subspaces. Next, it trains semi-supervised linear classifiers on graphs that are constructed by these coefficients. Finally, it combines these classifiers into an ensemble classifier by minimizing a linear regression problem. Unlike traditional graph-based semi-supervised classification methods, the graphs of SSC-SSR are data-driven instead of man-made in advance. Empirical study on face images classification tasks demonstrates that SSC-SSR not only has superior recognition performance with respect to competitive methods, but also has wide ranges of effective input parameters.
Language eng
DOI 10.1007/s10115-013-0702-2
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30060823

Document type: Journal Article
Collection: School of Information Technology
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